面向无蜂窝大规模MIMO的分布式智能内容缓存与用户关联联合优化方法
Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO
- 电子学报 2025年53卷第6期 页码：1932-1942
- 作者机构：
1.重庆邮电大学通信与信息工程学院，重庆 400065
2.重庆邮电大学计算机科学与技术学院，重庆 400065
3.中国电子科技集团公司第十五研究所，北京 100083
- 作者简介：
[ "李云 男，1974年5月出生于四川省南充市.现为重庆邮电大学教授、博士生导师.主要方向为云计算、边缘智能、无线网络资源管理等.E-mail: liyun@cqupt.edu.cn" ]
[ "夏士超 男， 1991年1月出生于山东省德州市. 现为重庆邮电大学讲师、硕士生导师. 主要研究方向为边缘智能、通感算一体化、无线网络资源管理等. Email:xiashichao@cqupt.edu.cn" ]
- 基金信息：国家自然科学基金(62301099;62071077);中国博士后科学基金(2023MD734137)
DOI：10.12263/DZXB.20240867
中图分类号： TN929.5收稿：2024-09-20，
修回：2025-05-22，
纸质出版：2025-06-25
- 稿件说明：
移动端阅览
李云, 张承宇, 姚枝秀, 等. 面向无蜂窝大规模MIMO的分布式智能内容缓存与用户关联联合优化方法[J]. 电子学报, 2025, 53(06): 1932-1942.
LI Yun, ZHANG Cheng-yu, YAO Zhi-xiu, et al. Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO[J]. Acta Electronica Sinica, 2025, 53(06): 1932-1942.
李云, 张承宇, 姚枝秀, 等. 面向无蜂窝大规模MIMO的分布式智能内容缓存与用户关联联合优化方法[J]. 电子学报, 2025, 53(06): 1932-1942. DOI：10.12263/DZXB.20240867
LI Yun, ZHANG Cheng-yu, YAO Zhi-xiu, et al. Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO[J]. Acta Electronica Sinica, 2025, 53(06): 1932-1942. DOI：10.12263/DZXB.20240867
摘要
在无蜂窝大规模MIMO（Cell-Free massive Multiple-Input Multiple-Output， CF-mMIMO）网络环境中，业务需求差异化、环境高度动态化以及资源部署去中心化等特征，制约了CF-mMIMO缓存部署和分发过程中多维网络资源的分配效率.为此，本文对去中心化CF-mMIMO场景中的多样化内容缓存和多用户关联问题展开研究.首先，基于CF-mMIMO场景中内容缓存与用户关联间的耦合关系，研究并建立了内容缓存、用户关联和多维资源分配模型.其次，针对随机时变的网络环境和不完备的网络状态观测，以最大化网络能效为目标，将内容缓存、用户关联和资源分配问题抽象为分布式部分可观测马尔科夫决策过程.而考虑到多样化内容缓存需求和广域差异化网络空间特征，进一步提出一种基于图注意力网络的多智能体深度强化学习算法对内容缓存、用户关联和多维资源分配进行策略学习和优化.最后，仿真结果验证了所提算法在网络能效、系统吞吐量、缓存命中率方面具有明显的性能提升.
Abstract
In the cell-free massive MIMO (CF-mMIMO) networks
characterized by differentiated service requirements
highly dynamic conditions
and decentralized resource deployment
the efficiency of distributing multi-dimensional network resources during CF-mMIMO caching deployment is constrained. To address this
this paper conducts research on the problem of diverse content caching and multi-user association in decentralized CF-mMIMO scenarios. First
based on the coupling relationship between content caching and user association
models for content caching
user association
and multi-dimensional resource allocation are studied and established. Second
given the stochastic and time-varying network environment and incomplete network state observations
the content caching
user association
and resource allocation problem are abstracted as a distributed partially observable Markov decision process (POMDP) with the objective of maximizing network efficiency. Taking into account the diverse content caching requirements and wide spatial differentiation
a multi-agent deep reinforcement learning algorithm based on graph attention network is further proposed for strategic learning and optimization of content caching
user association
and multi-dimensional resource allocation. Finally
simulation results confirm that the proposed algorithm significantly enhances performance in terms of network efficiency
system throughput and cache hit rate.
关键词
Keywords
references
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